Evaluation of WHO Criteria for Viral Failure in Patients on Antiretroviral Treatment in Resource-Limited Settings

Our objective was to evaluate outcomes in patients with sustained viral suppression compared to those with episodes of viremia. Methods. In a prospective cohort of patients started on ART in Uganda and followed for 48 months, patients were categorized according to viral load (VL): (1) sustained-suppression: (VL ≤1,000 copies/mL) (2) VL 1,001–10,000, or (3) VL >10,000. Results. Fifty-Three (11.2%) and 84 (17.8%) patients had a first episode of intermediate and high viremia, respectively. Patients with sustained suppression had better CD4+ T cell count increases over time compared to viremic patients (P < .001). The majority of patients with viremia achieved viral suppression when the measurement was repeated. Only 39.6% of patients with intermediate and 19.1% with high viremia eventually needed to be switched to second line (P = .008). Conclusions. The use of at least one repeat measurement rather than a single VL measurement could avert from 60% to 80% of unnecessary switches

Development of artificial neural network models for paediatric critical illness in South Africa

OBJECTIVES: Failures in identification, resuscitation and appropriate referral have been identified as significant contributors to avoidable severity of illness and mortality in South African children. In this study, artificial neural network models were developed to predict a composite outcome of death before discharge from hospital or admission to the PICU. These models were compared to logistic regression and XGBoost models developed on the same data in cross-validation. DESIGN: Prospective, analytical cohort study. SETTING: A single centre tertiary hospital in South Africa providing acute paediatric services. PATIENTS: Children, under the age of 13 years presenting to the Paediatric Referral Area for acute consultations. OUTCOMES: Predictive models for a composite outcome of death before discharge from hospital or admission to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 765 patients were included in the data set with 116 instances (15.2%) of the study outcome. Models were developed on three sets of features. Two derived from sequential floating feature selection (one inclusive, one parsimonious) and one from the Akaike information criterion to yield 9 models. All developed models demonstrated good discrimination on cross-validation with mean ROC AUCs greater than 0.8 and mean PRC AUCs greater than 0.53. ANN1, developed on the inclusive feature-et demonstrated the best discrimination with a ROC AUC of 0.84 and a PRC AUC of 0.64 Model calibration was variable, with most models demonstrating weak calibration. Decision curve analysis demonstrated that all models were superior to baseline strategies, with ANN1 demonstrating the highest net benefit. CONCLUSIONS: All models demonstrated satisfactory performance, with the best performing model in cross-validation being an ANN model. Given the good performance of less complex models, however, these models should also be considered, given their advantage in ease of implementation in practice. An internal validation study is now being conducted to further assess performance with a view to external validation

Elicitation of domain knowledge for a machine learning model for paediatric critical illness in South Africa

OBJECTIVES: Delays in identification, resuscitation and referral have been identified as a preventable cause of avoidable severity of illness and mortality in South African children. To address this problem, a machine learning model to predict a compound outcome of death prior to discharge from hospital and/or admission to the PICU was developed. A key aspect of developing machine learning models is the integration of human knowledge in their development. The objective of this study is to describe how this domain knowledge was elicited, including the use of a documented literature search and Delphi procedure. DESIGN: A prospective mixed methodology development study was conducted that included qualitative aspects in the elicitation of domain knowledge, together with descriptive and analytical quantitative and machine learning methodologies. SETTING: A single centre tertiary hospital providing acute paediatric services. PARTICIPANTS: Three paediatric intensivists, six specialist paediatricians and three specialist anaesthesiologists. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The literature search identified 154 full-text articles reporting risk factors for mortality in hospitalised children. These factors were most commonly features of specific organ dysfunction. 89 of these publications studied children in lower- and middle-income countries. The Delphi procedure included 12 expert participants and was conducted over 3 rounds. Respondents identified a need to achieve a compromise between model performance, comprehensiveness and veracity and practicality of use. Participants achieved consensus on a range of clinical features associated with severe illness in children. No special investigations were considered for inclusion in the model except point-of-care capillary blood glucose testing. The results were integrated by the researcher and a final list of features was compiled. CONCLUSION: The elicitation of domain knowledge is important in effective machine learning applications. The documentation of this process enhances rigour in such models and should be reported in publications. A documented literature search, Delphi procedure and the integration of the domain knowledge of the researchers contributed to problem specification and selection of features prior to feature engineering, pre-processing and model development

Integration of antenatal syphilis screening in an urban HIV clinic: a feasibility study.

BACKGROUND: Syphilis infection during pregnancy leads to avoidable morbidity and mortality and remains a significant problem in sub-Saharan Africa. Despite global initiatives to increase the proportion of pregnant women screened, implementation has been slow. We sought to investigate the feasibility of adding syphilis screening within an integrated antenatal HIV clinic. METHODS: Pregnant women attending the HIV antenatal clinic were sequentially enrolled and consenting participants answered a questionnaire on sexual behavior and previous pregnancies, provided sociodemographic data, and were tested using rapid plasmin reagin (RPR). If positive, participants were treated with benzathine penicillin. All were given a partner notification slip and were followed up after delivery to determine birth outcomes. RESULTS: 584 of 606 (95.7%) women approached and consented to test for syphilis. 570 women were enrolled (median age 29 (IQR 25-32) with a median (IQR) CD4 of 372 (257-569) cells/μL). Of the 5.1% (29/570) with a positive RPR, all were asymptomatic, were successfully contacted, and treated with benzathine penicillin without adverse reactions. Overall, 61 (12.1%) of the participants had an adverse birth outcome. In the bivariate analysis, only age was significantly different between those with and without a positive RPR (RR = 1.15, 95% CI 1.065-1.248; p < 0.001). Partners of only 10 (34.5%) participants returned for treatment. CONCLUSIONS: Structural interventions such as opt-out testing for syphilis within integrated HIV-antenatal care clinics are feasible and capitalize on the excellent care programs that have already been established for HIV care. Novel approaches are required for partner notification

Knowledge and perceptions of brucellosis in the pastoral communities adjacent to Lake Mburo National Park, Uganda

BACKGROUND: Brucellosis is one of the most common zoonotic infections globally. Lack of knowledge about brucellosis may affect the health-seeking behavior of patients, thus leading to sustained transmission in these communities. Our study assessed knowledge and perceptions of brucellosis among pastoral communities adjacent to Lake Mburo National Park (LMNP), Kiruhura District, Uganda. METHODS: A community cross-sectional questionnaire survey involving 371 randomly selected household heads from three sub-counties neighboring LMNP were interviewed between June and August 2012. Data collected included communities’ knowledge on causes, symptoms, transmission, treatment, prevention and risk factors of brucellosis. Multivariable logistic regression analysis was performed to explore strength of association between overall knowledge of brucellosis and various individual factors using odds ratios and 95% confidence intervals. RESULTS: Only 70 (19%) knew the symptoms of brucellosis in animals, and three quarters (279, 75.5%) mentioned joint and muscle pain as a common symptom in humans. Almost all participants (370, 99.3%) had ever heard about brucellosis, majority (311, 84.7%) believed it affects all sexes and two thirds (67.7%) of the respondents believed close proximity to wildlife contributes to the presence of the disease. Almost all (352, 95.4%) knew that brucellosis in humans could be treatable using modern drugs. The main routes of infection in humans such as consumption of unpasteurized dairy products were known by 97% (360/371); eating of half-cooked meat by 91.4% and eating contaminated pasture in animals by 97.4%. There was moderate overall knowledge of brucellosis 197 (53.1%). Factors associated with higher overall knowledge were being agro-pastoralists (aOR: 2.08, CI: 1.17-3.71) compared to pure pastoralists while those who reported that the disease was a health problem (aOR: 0.18, CI: 0.06-0.56) compared to those who said it was not were less likely to be knowledgeable. CONCLUSIONS: There was moderate overall knowledge of human and animal brucellosis among the participants. Majority of the participants believed that close proximity to wildlife contributes to the presence of the disease in the area. There is a need for collaboration between the public health, veterinary and wildlife sectors to provide health education on brucellosis for better management of the disease in the communities

The effects of longitudinal HIV viral load exposure on immune outcomes, mortality, and opportunistic infections in people on ART in sub-Saharan Africa

Thesis (PhD)--Stellenbosch University, 2017.ENGLISH SUMMARY : Introduction: Longitudinal viral load monitoring is used as a cross-sectional marker for treatment failure in HIV infected people receiving antiretroviral therapy. Cumulative viral load, as quantified by area under the viral load curve during combination antiretroviral therapy, has been correlated with treatment outcomes in studies outside, but not within, sub-Saharan Africa. We investigate the effects of exposure to longitudinal viral load on, the incidence of opportunistic infections, mortality and immune recovery in local, previously combination antiretroviral therapy naïve, cohorts. Further, we systematically review statistically derived immune response models and use this to define priors for Bayesian models for application on a previously undescribed treatment cohort. Methods: We analyze data from the Infectious Diseases Institute (IDI) cohort, Kampala-Uganda, and the Antiretroviral Clinic at Tshwane District Hospital in Gauteng-South Africa. For the systematic review, we use ‘Preferred Reporting Items for Systematic Review and Meta- Analyses’ guidelines. We also compare cumulative viral load as numerically estimated using two methods: area under the viral load curve, which is then log-transformed, named, ‘untransformed cumulative viral load’; and area under the log-transformed viral load curve, above the kit-based detection limit of 400 copies/mL, named, ‘transformed cumulative viral load’. We use Cox Proportional Hazards and Bayesian Generalized Mixed Effects to define treatment outcome models. Results: In the IDI cohort most recent viral load, not cumulative viral load, is associated with a 1.34-fold (95% confidence interval: 1.12, 1.61) increase in the risk of opportunistic infections. Transformed, not untransformed, cumulative viral load is associated with mortality and immune response. Each log10 copy-yr/mL increase corresponds to a 1.63-fold (95% confidence interval: 1.02, 2.60) increase in risk of mortality. Systematic review of immune response statistical models also reveals many differences in the number and type of variables adjusted-for, variable transformations and scales and scant details regarding the modelling methods employed. In the Tshwane cohort, using Bayesian methods, for the slope of longitudinal CD4 counts, each log10 copy-yr/mL increase cumulative viral load corresponds to a mean annual CD4 count decrease of -19.5 cells/μL (95% credible interval: -28.34, -10.72). Further, in the asymptote model, each log10 copy-yr/mL increase reduced the odds of having a CD4 count ≥500 cells/μL to 0.42 (95% credible interval: 0.242, 0.724). Modelling inherently variable absolute CD4 count using a Student’s t-distribution produced better fits than assuming a Gaussian normal distribution. Discussion: Transformed cumulative viral load is associated with both mortality and long-term immune response, while most recent viral load is associated with incidence of opportunistic infections. This thesis emphasizes the need for the review of existing literature prior to any statistical analyses, so that more comparable and robust statistical models than have been available to date will be constructed. In particular, comparing immunological outcomes (CD4 counts), statistical models for sub-Saharan African cohorts would benefit from the application of more uniform modelling techniques. Adjusting for transformed cumulative viral load and the use of appropriate distributional assumptions, improves the modelling of immune response to antiretroviral therapy. Future statistical immune response models would benefit from the use of Bayesian methods owing to their flexibility in the selection of prior distributions and hierarchical model designs.AFRIKAANSE OPSOMMING : Inleiding: Die monitering van longitudinale viruslading word gebruik as 'n biomerker vir behandelingsfaling in HIV-geïnfekteerde mense wat kombinasie antiretrovirale terapie ontvang. Kumulatiewe viruslading, gekwantifiseer as die area onder die viruslading kurwe tydens terapie, is gekorreleer met behandelingsuitkomste in studies elders, maar nie in Afrika suid van die Sahara. In hierdie studie word die effek van longitudinale viruslading, insluitend die voorkoms van opportunistiese infeksies, sterfte en die herstel van die immuunstelsel in plaaslik behandelde pasiënte ondersoek. Verder, word ʼn sistematiese oorsig van statistiese immuunrespons modelle uitgevoer. Hierdie resultate word gebruik om Bayesiaanse modelle te definieer, vir toepassing op 'n voorheen onbeskrewe pasiënt groep. Metodes: Kohorte van die Infectious Diseases Institute (IDI), in Kampala, Uganda, en van die Antiretrovirale Kliniek by die Tshwane Distrikshospitaal in Gauteng, Suid-Afrika is geanaliseer. Vir die sistematiese oorsig gebruik ons die sogenaamde ‘Preferred Reporting Items for Systematic Review and Meta-Analyses’ riglyne. Ons vergelyk ook kumulatiewe virusladings beraam met twee numeriese metodes: 1) die log-getransformeerde area onder die viruslading kurwe, genaamd 'ongetransformeerde kumulatiewe viruslading'; en 2) die area onder die log-getransformeerde viruslading kurwe, bo die toets-spesifieke limiet van deteksie van 400 kopieë/ml, genaamd die 'getransformeerde kumulatiewe viruslading'. Ons gebruik ‘Cox Proportional Hazards’ en ‘Bayesian Generalized Mixed Effects’ om behandelingsuitkoms modelle te definieer. Resultate: Vir die IDI kohort is die mees onlangse viruslading, i.e. die nie-kumulatiewe viruslading, geassosieer met 'n 1.34-voud toename (95% vertrouensinterval: 1.12, 1.61) in die risiko van opportunistiese infeksies. Die getransformeerde kumulatiewe viruslading is geassosieer met sterfte en immuunrespons. Elke log10 kopie/jr/ml verhoging stem ooreen met 'n 1.63-voud toename (95% vertrouensinterval: 1.02, 2.60) in die risiko vir sterfte. Die sistematiese oorsig van statistiese modelle vir immuunrespons het ook baie verskille getoon in die aantal en tipe veranderlikes waarvoor aangepas was, veranderlike transformasies en skale, en besonderhede was skaars oor die modelleringsmetodes wat gebruik was. In die Tshwane kohort, beraam met behulp van Bayesiaanse metodes, veroorsaak elke log10 kopie/jr/ml kumulatiewe viruslading toename 'n gemiddelde jaarlikse CD4-telling afname van -19.5 selle/μL (95% vertrouensinterval: -28.34, -10.72). Verder, in die asimptootmodel, verminder elke log10 kopie/jr/ml toename in viralelading die kans om 'n CD4-telling van meer as 500 selle/μL met 0.42 (95% vertrouensinterval: 0.242, 0.724). Die modellering van inherente veranderende CD4 telling het met behulp van 'n Student se t-verdeling beter modelle geproduseer as vir Gaussiese normaal verdelings. Bespreking: Getransformeerde kumulatiewe viruslading is geassosieer met beide sterfte en langtermyn immuunrespons, terwyl die mees onlangse viruslading verband hou met die voorkoms van opportunistiese infeksies. Hierdie proefskrif beklemtoon die vereiste vir ʼn oorskou van bestaande literatuur voordat enige statistiese ontledings onderneem word, sodat meer vergelykbare en robuuste statistiese modelle gebou sal word as wat tot dusver beskikbaar was. Die vergelyking van immuunrespons (CD4-telling) statistiese modelle in antiretrovirale terapie sal baat vind by die toepassing van meer eenvormige modelleringstegnieke. Sulke modelle is verbeter deur gebruik van getransformeerde kumulatiewe viruslading en meer akkurate verdelingsaannames. Toekomstige statistiese immuunrespons modelle sal ook baat vind by die gebruik van Bayesiaanse metodes as gevolg van hul aanpasbaarheid in terme van verdelings keuses en die implementasie van hiërargiese modelontwerpe

Systematic review of statistically-derived models of immunological response in HIV-infected adults on antiretroviral therapy in Sub-Saharan Africa

CITATION: Sempa, J. B., Ujeneza, E. L. & Niewoudt, M. 2017. Systematic review of statistically-derived models of immunological response in HIV-infected adults on antiretroviral therapy in Sub-Saharan Africa. PLoS ONE, 12(2):e0171658, doi:10.1371/journal.pone.0171658.The original publication is available at http://journals.plos.org/plosoneIntroduction: In Sub-Saharan African (SSA) resource limited settings, Cluster of Differentiation 4 (CD4) counts continue to be used for clinical decision making in antiretroviral therapy (ART). Here, HIV-infected people often remain with CD4 counts <350 cells/μL even after 5 years of viral load suppression. Ongoing immunological monitoring is necessary. Due to varying statistical modeling methods comparing immune response to ART across different cohorts is difficult. We systematically review such models and detail the similarities, differences and problems. Methods: ‘Preferred Reporting Items for Systematic Review and Meta-Analyses’ guidelines were used. Only studies of immune-response after ART initiation from SSA in adults were included. Data was extracted from each study and tabulated. Outcomes were categorized into 3 groups: ‘slope’, ‘survival’, and ‘asymptote’ models. Wordclouds were drawn wherein the frequency of variables occurring in the reviewed models is indicated by their size and color. Results: 69 covariates were identified in the final models of 35 studies. Effect sizes of covariates were not directly quantitatively comparable in view of the combination of differing variables and scale transformation methods across models. Wordclouds enabled the identification of qualitative and semi-quantitative covariate sets for each outcome category. Comparison across categories identified sex, baseline age, baseline log viral load, baseline CD4, ART initiation regimen and ART duration as a minimal consensus set. Conclusion: Most models were different with respect to covariates included, variable transformations and scales, model assumptions, modelling strategies and reporting methods, even for the same outcomes. To enable comparison across cohorts, statistical models would benefit from the application of more uniform modelling techniques. Historic efforts have produced results that are anecdotal to individual cohorts only. This study was able to define ‘prior’ knowledge in the Bayesian sense. Such information has value for prospective modelling efforts.http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171658Publisher's versio

Cumulative viral load as a predictor of CD4+T-cell response to antiretroviral therapy using Bayesian statistical models

INTRODUCTION: There are Challenges in statistically modelling immune responses to longitudinal HIV viral load exposure as a function of covariates. We define Bayesian Markov Chain Monte Carlo mixed effects models to incorporate priors and examine the effect of different distributional assumptions. We prospectively fit these models to an as-yet-unpublished data from the Tshwane District Hospital HIV treatment clinic in South Africa, to determine if cumulative log viral load, an indicator of long-term viral exposure, is a valid predictor of immune response. METHODS: Models are defined, to express 'slope', i.e. mean annual increase in CD4 counts, and 'asymptote', i.e. the odds of having a CD4 count ≥500 cells/μL during antiretroviral treatment, as a function of covariates and random-effects. We compare the effect of using informative versus non-informative prior distributions on model parameters. Models with cubic splines or Skew-normal distributions are also compared using the conditional Deviance Information Criterion. RESULTS: The data of 750 patients are analyzed. Overall, models adjusting for cumulative log viral load provide a significantly better fit than those that do not. An increase in cumulative log viral load is associated with a decrease in CD4 count slope (19.6 cells/μL (95% credible interval: 28.26, 10.93)) and a reduction in the odds of achieving a CD4 counts ≥500 cells/μL (0.42 (95% CI: 0.236, 0.730)) during 5 years of therapy. Using informative priors improves the cumulative log viral load estimate, and a skew-normal distribution for the random-intercept and measurement error results is a better fit compared to using classical Gaussian distributions. DISCUSSION: We demonstrate in an unpublished South African cohort that cumulative log viral load is a strong and significant predictor of both CD4 count slope and asymptote. We argue that Bayesian methods should be used more frequently for such data, given their flexibility to incorporate prior information and non-Gaussian distributions.status: publishe